Timely adjustment of operating strategies in drinking water treatment in response to water quality variations of both natural and anthropogenic causes is a grand technical challenge. One essential approach is to develop and apply integrated sensing, monitoring, and modeling technologies to provide early warning messages to plant operators. This paper presents a thorough literature review of the technical methods, followed by the development of a model-based decision support system (DSS). The DSS aims to aid water treatment operation via source water impact analysis. This model-based DSS featuring remote sensing and fast learning techniques can be easily applied by end-users and provide a visual depiction of spatiotemporal variation in water quality parameters of interest in source water. The system is able to forecast the trend of water quality one day into the future at a specific location and to nowcast water quality at water intake, thus helping the assessment of water quality in finished water against treatment objectives. The model-based DSS was assessed in a case study at a water treatment plant in Las Vegas, United States.
Keywords: Data Fusion; Decision Support Systems; Drinking Water; Forecasting Models; Machine Learning; Remote Sensing.